Representation and Application of Jacket Knowledge Based on Domain Ontology

2021 ◽  
Vol 14 (2) ◽  
pp. 77-88
Author(s):  
global Xiao-YuJiang
Keyword(s):  
Domain Ontology ◽  
Knowledge Based

Research on Semantic Retrieval System for the Document Knowledge Based on Domain Ontology

2011 ◽  
Vol 204-210 ◽  
pp. 2171-2175
Author(s):  
Zi Yu Liu ◽  
Dong Li Zhang ◽  
Xue Hui Li
Keyword(s):  
High Speed ◽  
Retrieval System ◽  
Domain Ontology ◽  
Experimental Results ◽  
Semantic Retrieval ◽  
High Speed Railway ◽  
Knowledge Based ◽  
Main Components ◽  
Railway Domain ◽  
Concept Similarity

Domain ontology can effectively organize the knowledge of that domain and make it easier to share and reuse. We can build domain ontology on thesaurus and thematic words and index document knowledge using domain ontology. Under which this paper designs a semantic retrieval system for the document knowledge based on domain ontology, and the system consists of four main components: ontology query, semantic precomputation for document and the concept similarity, semantic extended search and reasoning search. Finally, this paper makes an experiment on high-speed railway domain. The experimental results show that the developed semantic retrieval system can reach the satisfied recall and precision.

scholarly journals Expert System of Anti-Diabetic Medicine Selection

2021 ◽  
Vol 5 (2) ◽  
pp. 175-182
Author(s):  
Andi Hutami Endang
Keyword(s):  
Expert System ◽  
World Health Organization ◽  
Early Stage ◽  
Domain Ontology ◽  
World Health ◽  
Diabetic Medicine ◽  
Knowledge Based ◽  
Health Organization

The Prediction of World Health Organization (WHO) estimates that in 2030 the number of diabetics in Indonesia reached approximately 21.3 million people. Moreover, the development of medicine consumed by diabetics also varies. In this paper, we present a system that represents a diabetes expert into a knowledge based on the domain ontology. The early stage of the system is developing drugs ontology (including functions and contraindications) and patient ontology. Then, matching a weighted ontology will give drugs recommendations that are suitable with the patient's condition. The system is able to analyze diabetes symptoms to give drugs recommendations to the patient.

Knowledge Extraction from Domain-Specific Documents

2017 ◽  
pp. 106-123
Author(s):  
Ram Kumar ◽  
Shailesh Jaloree ◽  
R. S. Thakur
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Knowledge Bases ◽  
Domain Ontology ◽  
Knowledge Based Systems ◽  
Domain Specific ◽  
Knowledge Based ◽  
Representation Systems ◽  
Terminological Confusion ◽  
Knowledge Representation Systems

Knowledge-based systems have become widespread in modern years. Knowledge-base developers need to be able to share and reuse knowledge bases that they build. As a result, interoperability among different knowledge-representation systems is essential. Domain ontology seeks to reduce conceptual and terminological confusion among users who need to share various kind of information. This paper shows how these structures make it possible to bridge the gap between standard objects and Knowledge-based Systems.

Capturing Evolutional Knowledge Using Time Interval Tracing

2007 ◽  
Vol 11 (4) ◽  
pp. 373-380
Author(s):  
Shun-Chieh Lin ◽  
◽  
Chia-Wen Teng ◽  
Shian-Shyong Tseng ◽  
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Time Domain ◽  
Domain Knowledge ◽  
Domain Ontology ◽  
Time Interval ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Over Time

Knowledge acquisition is a critical bottleneck in building a knowledge-based system. Much research and many tools have been developed to acquire domain knowledge with embedded rules that may be ignored in constructing the initial prototype. Due to different backgrounds and dynamic knowledge changing over time, domain knowledge constructed at one time may be degraded at any time thereafter. Here, we propose knowledge acquisition, called enhanced embedded meaning capturing under uncertainty deciding (enhanced EMCUD), which constructs a domain ontology and traces information over time to efficiently update time-related domain knowledge based on the current environment. We enrich the knowledge base and ease the construction of domain knowledge that changes with times and the environment.

Analysis of Personalized Recommendation Models Based on Multi-Dimensional Ontology

2014 ◽  
Vol 998-999 ◽  
pp. 1347-1351
Author(s):  
Xiao Yan Yuan
Keyword(s):  
Domain Ontology ◽  
Personalized Recommendation ◽  
Single Dimension ◽  
Model Based ◽  
Knowledge Based ◽  
Multiple Dimensions ◽  
Information Recommendation

In order to solve the problem of information recommendation from single dimension, a personalized recommendation model based on multi-dimensional ontology is proposed in this paper. The necessity of multi-dimensional ontology is analyzed firstly. A multi-dimensional ontology, including domain ontology, time ontology and user ontology, etc., is then established using the examples of IT forefront knowledge. Based on this new ontology, recommendation of resources from multiple dimensions, i.e., the semantic, time, and comprehensive dimensions, is analyzed. Finally the personalized multi-dimensional recommendation model is presented.

scholarly journals Storing an OWL 2 Ontology in a Relational Database Structure

2015 ◽  
Vol 3 ◽  
pp. 71 ◽  
Author(s):  
Henrihs Gorskis ◽  
Arkady Borisov
Keyword(s):  
Relational Database ◽  
Domain Ontology ◽  
Database Management System ◽  
Software Library ◽  
Software Libraries ◽  
Knowledge Based ◽  
Ontology Language ◽  
Database Structure ◽  
Rdf Data ◽  
Language Specification

<p class="R-AbstractKeywords"><span lang="EN-GB">This paper examines the possibility of storing OWL 2 based ontology information in a classical relational database and reviews some existing methods for ontology databases. In most cases a database is a fitting solution for storing and sharing information among systems, clients or agents. Similarly, in order to make domain ontology information more accessible to systems, in a comparable way, it can be stored and provided in a database form. As of today, there is no consensus on a specific ontology database structure. The main focus of this paper is specifically on OWL 2 as a basis for the description of ontology centric information in a database. The Web Ontology Language OWL 2 is a language for describing ontology information for the Semantic Web. As such it consists of a list of reserved words and grammatical rules for defining many parts of ontology knowledge. Based on this language specification this paper examines the possibility of storing information in a relational database for the description of domain ontology information. By creating a database structure based on OWL2 it is feasible to obtain an approach to storing information about the domain ontology in an utilizable way, by using its descriptive abilities. Nowadays multiple approaches to storing ontology information and OWL in databases exist; most of them are based on storing RDF data or provide persistence for specific OWL software libraries. The examination of the existing approaches provided in this paper, shows how they differ from the goal of obtaining a general, more easily usable and less software library specific database for domain ontology centric information. This paper describes a version of a simple relational database capable of holding and providing ontology knowledge on demand, which can be implemented on a database management system of choice. </span></p>

Design Knowledge Retrieval Research Based on Domain Ontology

2014 ◽  
Vol 568-570 ◽  
pp. 1634-1638
Author(s):  
Mei Jia Zhao ◽  
Lei Wang
Keyword(s):  
Domain Ontology ◽  
Retrieval Algorithm ◽  
Design Knowledge ◽  
Frequency Interval ◽  
Retrieval Model ◽  
Knowledge Retrieval ◽  
Space Model ◽  
Knowledge Based ◽  
Similarity Algorithm ◽  
Design Documents

In order to solve the problems that design documents have large amounts of knowledge but distributed in disorder and that designers can’t express the true retrieval intention, which lead to a lot of useless retrieval results, this paper studied a retrieval algorithm of design knowledge based on domain ontology, and a design knowledge retrieval model based on domain ontology was established. On this basis, considering from word frequency, interval feature and semantic extension, the weight of which were taken into account for establishing retrieved concept set and indexing feature set of design documents; And according to vector space model, an improved similarity algorithm was used to match these set .Ultimately, the design knowledge matching with the retrieval intention was obtained, and the recall and precision of knowledge retrieval was improved greatly, thus achieving the purpose of knowledge reuse.

Is the Personal Always Political?

2017 ◽  
Vol 38 (3) ◽  
pp. 133-143 ◽  
Author(s):  
Danny Osborne ◽  
Yannick Dufresne ◽  
Gregory Eady ◽  
Jennifer Lees-Marshment ◽  
Cliff van der Linden
Keyword(s):  
Survey Data ◽  
National Survey ◽  
Negative Relationship ◽  
Openness To Experience ◽  
Political Sophistication ◽  
Knowledge Based

Abstract. Research demonstrates that the negative relationship between Openness to Experience and conservatism is heightened among the informed. We extend this literature using national survey data (Study 1; N = 13,203) and data from students (Study 2; N = 311). As predicted, education – a correlate of political sophistication – strengthened the negative relationship between Openness and conservatism (Study 1). Study 2 employed a knowledge-based measure of political sophistication to show that the Openness × Political Sophistication interaction was restricted to the Openness aspect of Openness. These studies demonstrate that knowledge helps people align their ideology with their personality, but that the Openness × Political Sophistication interaction is specific to one aspect of Openness – nuances that are overlooked in the literature.

Connectives Increase Knowledge-Based Inferences

1994 ◽  
Author(s):  
Gregory Barker ◽  
Keith Millis ◽  
Jonathan M. Golding
Keyword(s):  
Knowledge Based

The contribution of low-level sensory features versus knowledge-based factors for encoding of objects in working memory

2013 ◽  
Author(s):  
Valerio Santangelo ◽  
Simona Arianna Di Francesco ◽  
Serena Mastroberardino ◽  
Emiliano Macaluso
Keyword(s):  
Working Memory ◽  
Low Level ◽  
Knowledge Based ◽  
Sensory Features
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